The manipulation of crop genetics for the optimization of agronomic traits has resulted in a revolution in the seed industry. However, as many as 98% of these agronomic traits are quantitative traits, such that they are controlled by two or more genes and have measurable variability among the individual phenotypes. In order to understand and control the inheritance of these genes and the resultant phenotypes, scientists in the field have traditionally utilized methods such as quantitative trait locus (QTL) analysis.
As an outcome of QTL analysis, scientists identify chromosomal regions that are in close proximity to genes controlling a trait of interest. These chromosomal regions may be the target gene itself or may be genetic markers such as RFLPs, AFLPs, RAPDs, VNTRs, microsatellite polymorphisms, SNPs, and STRs. Because the markers are in close proximity to the genes, they tend to be inherited along with the gene (a phenomenon known as genetic linkage). As a result, the marker can be used to track the inheritance of the genes of interest. The process and statistical methods used to identify the location and effects of the various genes of interest or markers associated with these genes is called QTL mapping.
A major limitation of QTL analysis is that it has historically been limited to populations derived from single crosses of inbred lines and thus has not incorporated the extensive plant pedigree data that has been collected by plant breeders. Previous attempts to explicitly include this information have proven to be computationally intensive and are difficult to parameterize. As a result, vast amounts of data collected over the course of the development of modern elite plant varieties is essentially unused for purposes of phenotypic prediction.
Analysis of more complex populations derived from multiple founders or collected from ongoing breeding programs has the potential to significantly improve the understanding of important agronomic traits. For example, if the stability and magnitude of individual genes across different genetic backgrounds can be quantified more accurately, improved response to selection can be obtained. The use of these more complex populations would provide better context for making inferences by better approximating commercial breeding populations and would increase cost effectiveness by allowing use of available phenotypes from ongoing selection experiments. While some efforts have been undertaken to use historical pedigree data to predict phenotypes within mapping populations, those efforts have been largely unproductive in large part because of the inability to efficiently use the extensive pedigree and marker information that must be combined and analyzed.
The important benefits that can be derived from incorporation of comprehensive pedigree data coupled with the inability of those skilled in the art to effectively perform such analysis underscores the long felt, unsatisfied need for such tools in the art.